Social network optimization

Abstract

In one embodiment, a computing system may access first data associated with a first user. The system may determine, based on the first data associated with the first user, a number of content recommendations for the first users. The content recommendations may be associated with one or more interests or one or more operations of the first user. The system may execute one or more operations associated with the content recommendations. The one or more operations may cause one or more contents to be displayed to the first user.

What is claimed is: 
     
         1 . A method, by one or more computing systems, comprising:
 accessing first data associated with a first user;   determining, based on the first data associated with the first user, a plurality of content recommendations for the first users, the plurality of content recommendations being associated with one or more interests or one or more interactions of the first user with a social network platform; and   executing one or more operations associated with the plurality of content recommendations, the one or more operations causing one or more contents to be displayed to the first user.   
     
     
         2 . The method of  claim 1 , further comprising:
 determining grouping information associated with a plurality of first tabs grouped by the first user;   determining contents of interaction contexts and the plurality of first tabs in which the first user interacts with the plurality of first tabs;   using the grouping information, the contents, and the interaction contexts associated with the plurality of first tabs to train a machine-learning model to process content and interaction context associated with a tab to generate an embedding in a d-dimensional embedding space to represent the tab;   generating, for each of a plurality of second tabs, the embedding in the d-dimensional embedding space using the trained machine-learning model; and   grouping the plurality of second tabs based on the embeddings of the plurality of second tabs.   
     
     
         3 . The method of  claim 1  further comprising, by a server of an online content provider:
 receiving, from an advertiser, information related to the advertiser and an advertisement; 
 receiving, from the advertiser, a bid amount to make the advertisement viewable by users of the online content provider; 
 allowing the first user to access the server of the online content provider to view options for filtering advertisements displayed by the online content provider and information indicating credit amounts that the first user would earn by watching advertisements displayed by the online content provider; 
 receiving, from the first user, filter selections for advertisements the first user wishes to watch while using a service provided by the online content provider, wherein the filter selections include the advertisement of the advertiser; 
 providing, to the first user, the advertisement made viewable by the advertiser; 
 sending, to the first user, a credit for watching the advertisement, wherein an amount of the credit earned by the first user is related to the bid amount by the advertiser to make the advertisement viewable; and 
 providing service or benefits to the first user based on the credit earned by the first user. 
 
     
     
         4 . The method of  claim 1 , further comprising:
 determining, based on a recommendation ruleset, a set of recommended media items for display to the first user using a plurality of views, wherein each view of the plurality of views is configured to display a subset of recommended media items in the set;   identifying, for each of the plurality of views, a pair of media items in the subset of recommended media items associated with the view, the pair of media items having a highest similarity score among pairs of media items in the subset; and   generating, based on a visual-diversity ruleset, visual-diversity scores for the plurality of views based on the highest similarity scores associated with the pairs of media items associated with the plurality of views.   
     
     
         5 . The method of  claim 1 , further comprising:
 determining an interest distribution of the first user based on engagement logs of the first user, wherein the interest distribution is in an N-dimensional interest space with each dimension corresponding to a content topic associated with the engagement logs of the first user;   determining a plurality of personalized topics for the first user based on the interest distribution of the first user and one or more user inputs for topic control;   generating a topic-content graph based on labeled contents of a content pool, wherein the topic-content graph comprises a plurality of topic nodes each representing a topic and a plurality of content nodes each representing a content, and wherein the plurality of topic nodes and the plurality of content nodes are connected with respective links; and   determining recommended contents for the first user based on the plurality of personalized topics and the topic-content graph, wherein the recommended contents are personalized for the first user.   
     
     
         6 . The method of  claim 5 , wherein the plurality of personalized topics are determined based on affinity analyzing results one a plurality of content classifications accessed from a taxonomy database. 
     
     
         7 . The method of  claim 5 , wherein the plurality of personalized topics are determined by a user-to-topic predictor.